Knowledge Graphs (KGs) often suffer from incomplete knowledge, which restricts their utility.Recently, Contrastive Learning (CL) has been introduced to Knowledge Graph Completion (KGC), significantly improving the discriminative capabilities of KGC models and setting new benchmarks in performance.However, existing contrastive methods primarily focus on individual triples, overlooking the broader structural connectivities of KGs.This narrow focus hampers a more comprehensive understanding of the graph's structural knowledge.To address this gap, we propose StructKGC, a novel contrastive learning framework designed to flexibly accommodate the diverse topologies inherent in KGs.We introduce four contrastive tasks tailored to KG data: Vertex-level CL, Neighbor-level CL, Path-level CL, and Relation composition level CL.These tasks are trained synergistically during the fine-tuning of pretrained language models (PLMs), allowing for a more nuanced capture of subgraph semantics.To validate the effectiveness of our method, we perform a comprehensive set of experiments on several real-world datasets.The experimental results demonstrate that our approach achieves SOTA performance under standard supervised and low-resource settings.Furthermore, we observe that the various structure-aware tasks introduced can mutually reinforce each other, resulting in consistent performance enhancements.

Abstract Task‐oriented dialogue system (TOD) is one kind of application of artificial intelligence (AI). The response generation module is a key component of TOD for replying to user's questions and concerns in sequential natural words. In the past few years, the works on response generation have attracted increasing research attention and have seen much progress. However, existing works ignore the fact that not each turn of dialogue history contributes to the dialogue response generation and give little consideration to the different weights of utterances in a dialogue history. In this article, we propose a hierarchical memory network mechanism with two steps to filter out unnecessary information of dialogue history. First, an utterance‐level memory network distributes various weights to each utterance (coarse‐grained). Second, a token‐level memory network assigns higher weights to keywords based on the former's output (fine‐grained). Furthermore, the output of the token‐level memory network will be employed to query the knowledge base (KB) to capture the dialogue‐related information. In the decoding stage, we take a gated‐mechanism to generate response word by word from dialogue history, vocabulary, or KB. Experiments show that the proposed model achieves superior results compared with state‐of‐the‐art models on several public datasets. Further analysis demonstrates the effectiveness of the proposed method and the robustness of the model in the case of an incomplete training set.

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs. At present, the prediction of GPCR interaction mainly uses machine learning methods. Machine learning methods generally require a large number of independent and identically distributed samples to achieve good results. However, the number of available GPCR samples that have been marked is scarce. Transfer learning has a strong advantage in dealing with such small sample problems. Therefore, this paper proposes a transfer learning method based on sample similarity, using XGBoost as a weak classifier and using the TrAdaBoost algorithm based on JS divergence for data weight initialization to transfer samples to construct a data set. After that, the deep neural network based on the attention mechanism is used for model training. The existing GPCR is used for prediction. In short-distance contact prediction, the accuracy of our method is 0.26 higher than similar methods.